Multiple censored data in dentistry: A new statistical model for analyzing lesion size in randomized controlled trials

Caries infiltration is a novel treatment option for proximal caries lesions. The idea is to build a diffusion barrier inside the lesion to slow down or stop the caries progression. If a lesion still reaches a critical size, restorative treatment is required. Clinical trials investigating caries infiltration thus produce multiple censored ordinal data. Standard statistical models do not take into account this censoring, and we therefore propose the Multiple Ordered Tobit (MOT) model. The model is implemented in R and compared with standard approaches. Simulation studies demonstrate that for all sample sizes and scenarios the MOT model has the largest statistical power among all methods compared, and it is robust against heteroscedasticity to some extent. Finally, a comparison with dichotomous and ordinal scaled models shows that the use of metric data for the lesion size reduces the required sample size considerably.